[lab-confidence-intervals]Celso Dias

your-code/main.ipynb

@@ -9,11 +9,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Libraries"
+    "# Libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import scipy.stats as st"
    ]
   },
   {
@@ -32,11 +35,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(172.14308590115726, 174.79024743217607)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "heights = np.array([167, 167, 168, 168, 168, 169, 171, 172, 173, 175, 175, 175, 177, 182, 195])\n",
+    "alpha=0.8\n",
+    "mean=np.mean(heights)\n",
+    "std=4\n",
+    "n=(len(heights)-1)\n",
+    "st.norm.interval(0.8,loc=mean,scale=std/np.sqrt(len(heights)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(170.9019616724727, 176.03137166086063)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#With t distribuiton because we dont have more than 30 \n",
+    "std=heights.std(ddof=1)\n",
+    "st.t.interval(0.8,n-1,loc=mean,scale=std/np.sqrt(len(heights)))"
    ]
   },
   {
@@ -51,11 +93,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Our CI for 80% proportion is [0.20248138545542083] [0.3118043288302934]\n",
+      "Our CI for 90% proportion is [0.18698561776452813] [0.3273000965211861]\n",
+      "Python Way (0.20248138545542083, 0.3118043288302934) \n",
+      " (0.1869856177645281, 0.3273000965211861)\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code \n",
+    "n=105\n",
+    "losses=27\n",
+    "p=losses/n\n",
+    "confidence_levels=[0.80, 0.90]\n",
+    "ste=2\n",
+    "#80% confidence\n",
+    "\n",
+    "z_value=st.norm.ppf(1-(1-0.80)/2)\n",
+    "\n",
+    "margin_of_error= z_value*np.sqrt((p*(1-p))/n)\n",
+    "lower_bound= p-margin_of_error\n",
+    "upper_bound=p+margin_of_error\n",
+    "\n",
+    "print(f'Our CI for 80% proportion is [{lower_bound}] [{upper_bound}]')\n",
+    "#90% confidence\n",
+    "\n",
+    "z_value=st.norm.ppf(1-(1-0.90)/2)\n",
+    "se=np.sqrt((p*(1-p))/n)\n",
+    "margin_of_error= z_value*se\n",
+    "lower_bound= p-margin_of_error\n",
+    "upper_bound=p+margin_of_error\n",
+    "\n",
+    "print(f'Our CI for 90% proportion is [{lower_bound}] [{upper_bound}]')\n",
+    "\n",
+    "#python way\n",
+    "\n",
+    "print('Python Way',\n",
+    "    st.norm.interval(0.80, loc = p, scale = se),'\\n',\n",
+    "    st.norm.interval(0.90, loc = p, scale = se))"
    ]
   },
   {
@@ -76,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -94,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -145,7 +227,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,